How to get the intercept from a linear model with lasso (lars R package)
intercept, lars, lasso-regression, r
Solution
`intercept=T` in `lars` has the effect of centering the x variables and y variable. It doesn't include an explicit intercept term with a coefficient.
That being said, you could do `predict(m,data.frame(a=0,b=0),s=2)$fit` to get the predicted value of y when the covariates are 0 (the definition of a traditional intercept)
Problem
I am having an hard time in getting the model estimated by the R package `lars` for my data. For example I create a fake dataset x and corresponding values y like this: ``` x = cbind(runif(100),rnorm(100)) colnames(x) = c("a","b") y = 0.5 + 3 * x[,1,drop = FALSE] ``` Next I train a model that uses lasso regularization using the lars function: ``` m = lars(x,y,type = "lasso", normalize = FALSE, intercept = TRUE) ``` Now I would like to know what is the estimated model (`that I know to be: y = 0.5 + 3 * x[,1] + 0 * x[,2]`) I am only interested in the coefficients obtained in the last step: ``` cf = predict(m, x, s=1, mode = "fraction", type = "coef")$coef cf a b 3 0 ``` These are the coefficients that I expect, but I can't find a way to get the intercept (`0.5`) from `m`. I have tried to check the code of `predict.lars`, where the fit is done as such: ``` fit = drop(scale(newx, object$meanx, FALSE) %*% t(newbetas)) + object$mu) ``` I can see that the variables are scaled, and that the mean of `y` (object$mu) is used, but I can't find an easy way to obtain the value of the intercept I am looking for. How can I get that?